Computer system design inevitably enters the landscape of design for sustainability as the data center power footprint has become a global concern. In the past ten years, Google server's electricity demand has increased almost 20-fold [1]. The huge IT energy consumption not only increases the total cost of ownership (TCO) but also leaves profound impact on the environment. According to a McKinsey Quarterly report, the annual CO2 emissions of computing systems will research 1.54 metric gigatons within eight years, which could make IT companies among the biggest greenhouse gas emitters by 2020 [2]. Consequently, renewable energy powered data centers are gaining growing popularity in both the IT industry and academia, as a way to tackle the dual challenges of reducing energy consumption and environmental issues [3-9].
Existing proposals on renewable energy-aware power management schemes largely emphasize adapting the computer load to the time-varying power budget [3-9]. We broadly categorize these techniques into two types: 1) load tuning based design; and 2) job scheduling based design. While the former approach leverages performance scaling techniques (e.g., DVFS and server power state tuning) to track the time-varying renewable power budget [3-5], the later approach schedules job requests based on the renewable energy availability [6-9]. Since these techniques are driven by variable and intermittent power supply, they typically suffer extended job turnaround time. They can hardly maintain desired instantaneous throughput and service availability without substantial utility grid support.
The IT industry is actively looking for opportunities in non-conventional power provisioning solutions such as distributed generation.
Distributed generation (DG) refers to a variety of small, modular electric generators near the point of use. In recent years, DG has gained tremendous interest as an alternative source of power for IT industry. According to the U.S. Environmental Protection Agency (EPA), using DG in data center design could achieve great energy savings, significant environmental benefits, and high power reliability [11].
As shown in FIG. 1, DG system encompasses a wide range of green energy technologies, such as photovoltaic module (PV), wind power, fuel cell, and bio-fuel based gas turbine. While PV/wind power depends on environmental condition, the outputs of fuel cells and gas turbines are tunable. They can provide a key supporting service called load following [12], which refers to the use of online generation equipment to track the changes in customer loads. Therefore, one can take advantage of the load following capabilities of these tunable DG systems to meet the time-varying IT power demand. Such design is non-trivial because it enables data center to run on renewable energy sources without compromising workload performance.
When employing distributed generation to build a better data center power provisioning architecture, challenges arise due to the unpredictable and fluctuating data center load. FIG. 2 shows typical load following scenario that tracks customer load every 30 minutes. As can be seen, DG systems cannot provide fine-grained load demand following due to their limited response speed.
To handle the moment-to-moment load power demand, DG systems typically rely on large energy storage elements [12]. Such design not only increases the TCO (due to storage cost), but also incurs up to 25% roundtrip energy loss. More importantly, without careful power management, the disturbing load can cause frequent and excessive battery discharging actives, which may degrade the lifetime of these expensive electrical elements and quickly deplete the stored energy that is crucial for handling emergencies.
Distributed generation (DG) [10, 11] is an emerging trend of generating power locally to provide reliable, secure, and sustainable electrical energy to its consumers. Distributed generation encompasses several promising clean energy technologies such as gas turbine, biomass power, and fuel cell. These non-conventional power generators are known as microsources or distributed energy resources (DERs), and are typically Modular units of small capacity (typically between several kilowatts to tens of megawatts) [16].
In order to harness clean energy from DERs, microgrid is proposed as a local electricity distribution network that focuses on flexible and intelligent management of DG systems. The responsibility of microgrid is to dynamically control the power flow in response to any disturbance and load changes. Although microgrid can import/export power from/to the utility power line, it is usually the last resort due to the low transmission efficiency, high peak power cost, and sustainability consideration [12].
In contrast with a conventional bulk grid, which has large amount of capacity inertia, distributed generation typically does not have reserved capacity [11]. The supply and storage of energy is generally planned carefully in microgrid to ensure instantaneous demand and long-term energy balance [10].
In Table 1 we show the response speed of typical DG systems. Most energy storage devices have very fast response speed that could release power almost immediately (in ms level). As a result, they are widely used to handle moment-to-moment load oscillation and disturbances, which is referred to as regulation [12]. In contrast, gas turbines and fuel cells are typically too slow to meet the load power variation since the change of the engine speed or the chemical reaction in the fuel requires time. Therefore, they are generally used to track the intra- and inter-hour changes in customer loads, which is referred to as load following [12].
Although there is no strict rule to define the temporal boundary between regulation and load following, typically load following occurs every 10˜15 minutes or more. It is not usually economically feasible to frequently adjust the output of distributed generators due to the increased performance cost and decreased fuel utilization efficiency.
The energy balance issue arises due to the fluctuating load in data centers and other computerized environments. Dynamic power tuning techniques (e.g., DVFS), frequent on/off power cycles, stochastic user requests, and data migration activities can cause varying degree of load power variation. Since distributed generators are generally placed near or at the point of energy consumption, they are often exposed to the full fluctuation of local IT loads rather than experiencing the averaging effect seen by larger, centralized power system. As a result, energy balancing becomes rather difficult in distributed generation powered data centers.
TABLE 1Response speed of DG systemsDG SystemsResponseStartupLead-acid batteryImmediateN/AFlywheelImmediateN/AFuel cell30 sec~5 min20~50 minGas turbine10 s of seconds 2~10 min
Accordingly, there exists a need in the art for optimization to improve efficiency and workload performance in DG systems.